{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01ddc0fa-f59a-434b-aa10-edf30c5b4bb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:11.863741Z",
     "iopub.status.busy": "2022-07-20T00:05:11.863147Z",
     "iopub.status.idle": "2022-07-20T00:05:13.536802Z",
     "shell.execute_reply": "2022-07-20T00:05:13.536134Z",
     "shell.execute_reply.started": "2022-07-20T00:05:11.863606Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import cv2\n",
    "import numpy as np\n",
    "import glob\n",
    "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "set_matplotlib_formats('svg')  # 展示的图片格式\n",
    "\n",
    "from albumentations import (\n",
    "    HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90, Resize, Normalize,\n",
    "    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,\n",
    "    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, RandomBrightnessContrast, IAAPiecewiseAffine,\n",
    "    IAASharpen, IAAEmboss, Flip, OneOf, Compose, ElasticTransform\n",
    ")\n",
    "from albumentations.pytorch import ToTensorV2\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b0ba54a-4245-486d-9376-623d0a9f006b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:13.538123Z",
     "iopub.status.busy": "2022-07-20T00:05:13.537992Z",
     "iopub.status.idle": "2022-07-20T00:05:13.686765Z",
     "shell.execute_reply": "2022-07-20T00:05:13.685992Z",
     "shell.execute_reply.started": "2022-07-20T00:05:13.538107Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ef87440a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"366.2pt\" height=\"161.391071pt\" viewBox=\"0 0 366.2 161.391071\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2022-07-20T08:05:13.621266</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>\n",
       "     </cc:Agent>\n",
       "    </dc:creator>\n",
       "   </cc:Work>\n",
       "  </rdf:RDF>\n",
       " </metadata>\n",
       " <defs>\n",
       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 161.391071 \n",
       "L 366.2 161.391071 \n",
       "L 366.2 0 \n",
       "L 0 0 \n",
       "L 0 161.391071 \n",
       "z\n",
       "\" style=\"fill: none\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 24.2 139.066071 \n",
       "L 359 139.066071 \n",
       "L 359 9.751786 \n",
       "L 24.2 9.751786 \n",
       "z\n",
       "\" style=\"fill: #ffffff\"/>\n",
       "   </g>\n",
       "   <g clip-path=\"url(#p9591f2f1e3)\">\n",
       "    <image xlink:href=\"data:image/png;base64,\n",
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      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread('./机动车车牌识别挑战赛公开数据/train/川A2C98C.jpg')\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "306b5ba4-b0fc-4e2d-bee4-2a09c6dab2d5",
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     "shell.execute_reply.started": "2022-07-20T00:05:13.693446Z"
    },
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   },
   "outputs": [],
   "source": [
    "paths = glob.glob('./机动车车牌识别挑战赛公开数据/train/*')\n",
    "labls = [x.split('/')[-1][:-4] for x in paths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcf27f41-3eb9-4180-b611-d1c238409391",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H5T贵B辽9赣闽C0YFGL蒙津7新J沪鲁8鄂浙X吉甘渝6S3U晋川陕VE皖黑粤WR冀A2QK豫D桂云1青P京4苏M湘NZ'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''.join(set(''.join(labls)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42db0523-dcdd-46b9-a7fd-d3137325fe90",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "皖    19526\n",
       "苏      462\n",
       "浙      196\n",
       "沪      103\n",
       "鄂       78\n",
       "豫       78\n",
       "粤       76\n",
       "鲁       74\n",
       "京       67\n",
       "赣       53\n",
       "闽       43\n",
       "陕       43\n",
       "湘       38\n",
       "冀       33\n",
       "川       32\n",
       "渝       27\n",
       "晋       24\n",
       "津       24\n",
       "辽       19\n",
       "黑        7\n",
       "云        6\n",
       "甘        5\n",
       "蒙        4\n",
       "贵        3\n",
       "新        3\n",
       "桂        2\n",
       "吉        2\n",
       "青        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([x[0] for x in labls]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "277d866f-d826-4b96-b5d4-20a0ce3329b3",
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     "shell.execute_reply.started": "2022-07-20T00:05:13.839337Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    18606\n",
       "N      291\n",
       "B      248\n",
       "D      217\n",
       "C      206\n",
       "H      205\n",
       "E      200\n",
       "K      160\n",
       "S      145\n",
       "M      125\n",
       "F      118\n",
       "G      101\n",
       "L       98\n",
       "Q       95\n",
       "J       76\n",
       "P       69\n",
       "R       53\n",
       "Y        7\n",
       "X        5\n",
       "T        3\n",
       "V        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([x[1] for x in labls]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a7f4f07-b53f-4ffb-864c-56465c4e5972",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(''.join(list(set(''.join(labls)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a391d4da-eacc-41cd-8a66-ffb5ca9180cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:13.978451Z",
     "iopub.status.busy": "2022-07-20T00:05:13.978331Z",
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     "shell.execute_reply.started": "2022-07-20T00:05:13.978436Z"
    },
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   "outputs": [],
   "source": [
    "from torch.utils.data.dataset import Dataset\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "import time\n",
    "import datetime\n",
    "import pdb\n",
    "import traceback\n",
    "\n",
    "import torch\n",
    "torch.manual_seed(0)\n",
    "torch.backends.cudnn.deterministic = False\n",
    "torch.backends.cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a64871d-75e3-4577-84ee-1adc30cd5c4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:14.047684Z",
     "iopub.status.busy": "2022-07-20T00:05:14.047454Z",
     "iopub.status.idle": "2022-07-20T00:05:14.108921Z",
     "shell.execute_reply": "2022-07-20T00:05:14.107968Z",
     "shell.execute_reply.started": "2022-07-20T00:05:14.047667Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class QRDataset(Dataset):\n",
    "    def __init__(self, img_paths, lbl_dict, transform=None, label=True):\n",
    "        self.img_paths = img_paths\n",
    "        self.lbl_dict = lbl_dict\n",
    "        self.label = label\n",
    "\n",
    "        if transform is not None:\n",
    "            self.transform = transform\n",
    "        else:\n",
    "            self.transform = None\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        start_time = time.time()\n",
    "\n",
    "        img = cv2.imread(self.img_paths[index])\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "        if self.transform:\n",
    "            augmented = self.transform(image=img)\n",
    "            img = augmented['image']\n",
    "\n",
    "        img_label = self.img_paths[index].split('/')[-1][:-4]\n",
    "        \n",
    "        if self.label:\n",
    "            label0 = np.array(self.char2idx(img_label[0]))\n",
    "            label1 = np.array(self.char2idx(img_label[1]))\n",
    "            label2 = np.array(self.char2idx(img_label[2]))\n",
    "            label3 = np.array(self.char2idx(img_label[3]))\n",
    "            label4 = np.array(self.char2idx(img_label[4]))\n",
    "            label5 = np.array(self.char2idx(img_label[5]))\n",
    "            label6 = np.array(self.char2idx(img_label[6]))\n",
    "\n",
    "            return img, torch.from_numpy(label0), torch.from_numpy(label1), \\\n",
    "                torch.from_numpy(label2), torch.from_numpy(label3), torch.from_numpy(label4),\\\n",
    "                torch.from_numpy(label5), torch.from_numpy(label6)\n",
    "        else:\n",
    "            return img\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.img_paths)\n",
    "\n",
    "    def char2idx(self, ch):\n",
    "        return self.lbl_dict.find(ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83e43383-40eb-4621-bf21-9833be5e0a84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:14.110579Z",
     "iopub.status.busy": "2022-07-20T00:05:14.110199Z",
     "iopub.status.idle": "2022-07-20T00:05:14.185441Z",
     "shell.execute_reply": "2022-07-20T00:05:14.184622Z",
     "shell.execute_reply.started": "2022-07-20T00:05:14.110545Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class RMB_Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(RMB_Net, self).__init__()\n",
    "\n",
    "        feat_size = 512\n",
    "        self.fc0 = nn.Linear(feat_size, 62)\n",
    "        self.fc1 = nn.Linear(feat_size, 62)\n",
    "        self.fc2 = nn.Linear(feat_size, 62)\n",
    "        self.fc3 = nn.Linear(feat_size, 62)\n",
    "        self.fc4 = nn.Linear(feat_size, 62)\n",
    "        self.fc5 = nn.Linear(feat_size, 62)\n",
    "        self.fc6 = nn.Linear(feat_size, 62)\n",
    "\n",
    "        model = models.resnet18(True)\n",
    "        model = torch.nn.Sequential(*(list(model.children())[:-1]))\n",
    "        self.resnet = model\n",
    "\n",
    "        self.resnet = model\n",
    "\n",
    "    def forward(self, img):\n",
    "        feat = self.resnet(img)\n",
    "        feat = feat.reshape(feat.size(0), -1)\n",
    "\n",
    "        out0 = self.fc0(feat)\n",
    "        out1 = self.fc1(feat)\n",
    "        out2 = self.fc2(feat)\n",
    "        out3 = self.fc3(feat)\n",
    "        out4 = self.fc4(feat)\n",
    "        out5 = self.fc5(feat)\n",
    "        out6 = self.fc6(feat)\n",
    "\n",
    "        return out0, out1, out2, out3, out4, out5, out6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ac7c911-2870-46ad-8755-58ccf54b12bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:14.186433Z",
     "iopub.status.busy": "2022-07-20T00:05:14.186192Z",
     "iopub.status.idle": "2022-07-20T00:05:14.264732Z",
     "shell.execute_reply": "2022-07-20T00:05:14.263720Z",
     "shell.execute_reply.started": "2022-07-20T00:05:14.186416Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(QRDataset(paths[:-500], \n",
    "                    'N鄂CD4云HSL湘渝5B苏贵浙TE晋P津X陕J青鲁K赣8辽FQ6R甘蒙G沪A冀吉新V9W1Y桂0黑闽豫Z粤京3川U72皖M',\n",
    "      Compose([\n",
    "                Resize(70, 200),\n",
    "                GridDistortion(p=.5, distort_limit=0.15, num_steps=5),\n",
    "                RandomBrightnessContrast(),\n",
    "                ElasticTransform(alpha=0.1, sigma=5, alpha_affine=2,),\n",
    "                Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "                ToTensorV2(),\n",
    "        ])),\n",
    "        batch_size=16, shuffle=True\n",
    ")\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(QRDataset(paths[-500:],\n",
    "                    'N鄂CD4云HSL湘渝5B苏贵浙TE晋P津X陕J青鲁K赣8辽FQ6R甘蒙G沪A冀吉新V9W1Y桂0黑闽豫Z粤京3川U72皖M',\n",
    "      Compose([\n",
    "                Resize(70, 200),\n",
    "                GridDistortion(p=.5, distort_limit=0.15, num_steps=5),\n",
    "                RandomBrightnessContrast(),\n",
    "                ElasticTransform(alpha=0.1, sigma=5, alpha_affine=2,),\n",
    "                Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "                ToTensorV2(),\n",
    "        ])),\n",
    "        batch_size=16, shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b29e6c8-15be-4a14-95f0-dca1a3d0e45a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:14.266502Z",
     "iopub.status.busy": "2022-07-20T00:05:14.266259Z",
     "iopub.status.idle": "2022-07-20T00:05:14.358838Z",
     "shell.execute_reply": "2022-07-20T00:05:14.358182Z",
     "shell.execute_reply.started": "2022-07-20T00:05:14.266485Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def accuracy(outputs, targets):\n",
    "    with torch.no_grad():\n",
    "        batch_size = outputs[0].size(0)\n",
    "        \n",
    "        output_idx = []\n",
    "        for output in outputs:\n",
    "            _, pred = output.topk(1, 1, True, True)\n",
    "            # pred = pred\n",
    "            pred = pred.t().flatten()\n",
    "            output_idx.append(pred.data.cpu().numpy())\n",
    "        \n",
    "        output_idx = np.vstack(output_idx)\n",
    "        targets = [x.data.cpu().numpy() for x in targets]\n",
    "        targets = np.vstack(targets)\n",
    "        return ((targets == output_idx).mean(0) == 1).mean(), (targets == output_idx).mean(0) != 1\n",
    "\n",
    "def train(train_loader, model, criterion, optimizer):\n",
    "    model.train()\n",
    "\n",
    "    train_acc = []\n",
    "    train_losss = []\n",
    "    for input,target0,target1,target2,target3,target4,target5,target6 in tqdm(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        input = input.cuda(non_blocking=True)\n",
    "        target0 = target0.cuda(non_blocking=True)\n",
    "        target1 = target1.cuda(non_blocking=True)\n",
    "        target2 = target2.cuda(non_blocking=True)\n",
    "        target3 = target3.cuda(non_blocking=True)\n",
    "        target4 = target4.cuda(non_blocking=True)\n",
    "        target5 = target5.cuda(non_blocking=True)\n",
    "        target6 = target6.cuda(non_blocking=True)\n",
    "\n",
    "        # compute output\n",
    "        output0,output1,output2,output3,output4,output5,output6 = model(input)\n",
    "        loss0 = criterion(output0, target0)\n",
    "        loss1 = criterion(output1, target1)\n",
    "        loss2 = criterion(output2, target2)\n",
    "        loss3 = criterion(output3, target3)\n",
    "        loss4 = criterion(output4, target4)\n",
    "        loss5 = criterion(output5, target5)\n",
    "        loss6 = criterion(output6, target6)\n",
    "\n",
    "        loss = (loss0+loss1+loss2+loss3+loss4+loss5+loss6)/7.0\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_losss.append(loss.item())\n",
    "        \n",
    "    return np.mean(train_losss)\n",
    "\n",
    "def validate(val_loader, model, criterion):\n",
    "    model.eval()\n",
    "    \n",
    "    val_acc = []\n",
    "    val_loss = []\n",
    "    val_error_idx = []\n",
    "    val_prob = []\n",
    "    with torch.no_grad():\n",
    "        for i, (input,target0,target1,target2,target3,target4,target5,target6) in enumerate(val_loader):\n",
    "            input = input.cuda(non_blocking=True)\n",
    "            target0 = target0.cuda(non_blocking=True)\n",
    "            target1 = target1.cuda(non_blocking=True)\n",
    "            target2 = target2.cuda(non_blocking=True)\n",
    "            target3 = target3.cuda(non_blocking=True)\n",
    "            target4 = target4.cuda(non_blocking=True)\n",
    "            target5 = target5.cuda(non_blocking=True)\n",
    "            target6 = target6.cuda(non_blocking=True)\n",
    "            \n",
    "            # compute output\n",
    "            output0,output1,output2,output3,output4,output5,output6 = model(input)\n",
    "            loss0 = criterion(output0, target0)\n",
    "            loss1 = criterion(output1, target1)\n",
    "            loss2 = criterion(output2, target2)\n",
    "            loss3 = criterion(output3, target3)\n",
    "            loss4 = criterion(output4, target4)\n",
    "            loss5 = criterion(output5, target5)\n",
    "            loss6 = criterion(output6, target6)\n",
    "            \n",
    "            loss = (loss0+loss1+loss2+loss3+loss4+loss5+loss6)/10.0\n",
    "            # measure accuracy and record loss\n",
    "            acc, error_idx = accuracy([output0,output1,output2,output3,output4,output5,output6], \n",
    "                            [target0,target1,target2,target3,target4,target5,target6])\n",
    "            \n",
    "            output_prob = None\n",
    "            for output in [output0,output1,output2,output3,output4,output5,output6]:\n",
    "                if output_prob is None:\n",
    "                    output_prob = np.exp(output.max(1)[0].data.cpu().numpy())\n",
    "                else:\n",
    "                    output_prob += np.exp(output.max(1)[0].data.cpu().numpy())\n",
    "            output_prob /= 7.0\n",
    "            \n",
    "            val_acc.append(acc)\n",
    "            val_loss.append(loss.item())\n",
    "            val_error_idx += list(error_idx)\n",
    "            val_prob += list(output_prob)\n",
    "\n",
    "\n",
    "        return np.mean(val_acc), np.mean(val_loss)\n",
    "        \n",
    "        \n",
    "def predict(test_loader, model):\n",
    "    model.eval()\n",
    "\n",
    "    test_prob = []\n",
    "    with torch.no_grad():\n",
    "        for i, input in enumerate(test_loader):\n",
    "            input = input.cuda(non_blocking=True)\n",
    "            \n",
    "            # compute output\n",
    "            output0,output1,output2,output3,output4,output5,output6 = model(input)\n",
    "            \n",
    "            output0 = output0.argmax(1).data.cpu().numpy()\n",
    "            output1 = output1.argmax(1).data.cpu().numpy()\n",
    "            output2 = output2.argmax(1).data.cpu().numpy()\n",
    "            output3 = output3.argmax(1).data.cpu().numpy()\n",
    "            output4 = output4.argmax(1).data.cpu().numpy()\n",
    "            output5 = output5.argmax(1).data.cpu().numpy()\n",
    "            output6 = output6.argmax(1).data.cpu().numpy()\n",
    "            \n",
    "            test_prob.append(\n",
    "                np.vstack([output0,output1,output2,output3,output4,output5,output6]).T\n",
    "            )\n",
    "\n",
    "    return test_prob\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa012634-d6bd-4363-8626-6f61bb340eda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:14.361869Z",
     "iopub.status.busy": "2022-07-20T00:05:14.361669Z",
     "iopub.status.idle": "2022-07-20T00:05:16.498570Z",
     "shell.execute_reply": "2022-07-20T00:05:16.497853Z",
     "shell.execute_reply.started": "2022-07-20T00:05:14.361852Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = RMB_Net()\n",
    "model = model.cuda()\n",
    "criterion = nn.CrossEntropyLoss().cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd4e966e-b085-47ee-8858-65f442c6c906",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:05:16.500092Z",
     "iopub.status.busy": "2022-07-20T00:05:16.499853Z",
     "iopub.status.idle": "2022-07-20T00:10:34.791381Z",
     "shell.execute_reply": "2022-07-20T00:10:34.790951Z",
     "shell.execute_reply.started": "2022-07-20T00:05:16.500075Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed91304c422d426faafc58f3799539c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1284 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.195415 0.398438 0.355179\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9724c17925bb46708f7ea50b989d5cbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1284 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.370024 0.689453 0.179101\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94d8b5eee7014701a5f96b826cc8ae1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1284 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.226887 0.761719 0.136990\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46c3c14d59a84c7daa4e75c9d6dddaf6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1284 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.180660 0.779297 0.129675\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a2bacce1eeb4d4986f9577dd254d71a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1284 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.151531 0.791016 0.110086\n"
     ]
    }
   ],
   "source": [
    "for _ in range(5):\n",
    "    train_loss = train(train_loader, model, criterion, optimizer)\n",
    "    val_acc, val_loss = validate(val_loader, model, criterion)\n",
    "    print('{:2f} {:2f} {:2f}'.format(train_loss, val_acc, val_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05236289-5ae3-494d-aade-1b7aac4c82e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:11:04.796810Z",
     "iopub.status.busy": "2022-07-20T00:11:04.796261Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_paths = glob.glob('./机动车车牌识别挑战赛公开数据/test/*')\n",
    "test_paths.sort()\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(QRDataset(test_paths,\n",
    "                    '0N鄂CD4云HSL湘渝5B苏贵浙TE晋P津X陕J青鲁K赣8辽FQ6R甘蒙G沪A冀吉新V9W1Y桂0黑闽豫Z粤京3川U72皖M',\n",
    "      Compose([\n",
    "                Resize(70, 200),\n",
    "                GridDistortion(p=.5, distort_limit=0.15, num_steps=5),\n",
    "                RandomBrightnessContrast(),\n",
    "                ElasticTransform(alpha=0.1, sigma=5, alpha_affine=2,),\n",
    "                Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "                ToTensorV2(),\n",
    "        ]), False),\n",
    "        batch_size=16, shuffle=False\n",
    ")\n",
    "\n",
    "test_pred = predict(test_loader, model)\n",
    "test_pred = np.vstack(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7dacc2c-8207-442d-9f44-45b43f8e4b55",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_texts = []\n",
    "for ts in test_pred[:]:\n",
    "    ts_text = ''\n",
    "    for x in ts:\n",
    "        ts_text += 'N鄂CD4云HSL湘渝5B苏贵浙TE晋P津X陕J青鲁K赣8辽FQ6R甘蒙G沪A冀吉新V9W1Y桂0黑闽豫Z粤京3川U72皖M'[x]\n",
    "    \n",
    "    test_texts.append(ts_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d9672f0-85e6-46b0-a5aa-4610ad984899",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.DataFrame([\n",
    "    [x.split('/')[-1] for x in test_paths],\n",
    "    test_texts\n",
    "]).T.to_csv('submit.csv', index=None, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a158307d-2c67-4f9a-879b-f7453652e581",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-20T00:00:46.799584Z",
     "iopub.status.busy": "2022-07-20T00:00:46.799057Z",
     "iopub.status.idle": "2022-07-20T00:00:46.806448Z",
     "shell.execute_reply": "2022-07-20T00:00:46.805540Z",
     "shell.execute_reply.started": "2022-07-20T00:00:46.799533Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 5000)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_texts), len(test_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82a26a72-c855-4944-86ca-584a1f68273b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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